Optimal targeting of nonlinear chaotic systems using a novel evolutionary computing strategy
•Control of chaotic systems is formulated as constrained optimization problem.•Teaching-learning-based optimization is to direct the chaotic series.•The efficacy of TLBO based optimal control technique have been demonstrated. Control of chaotic systems to given targets is a subject of substantial an...
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| Published in | Knowledge-based systems Vol. 107; pp. 261 - 270 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.09.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0950-7051 1872-7409 |
| DOI | 10.1016/j.knosys.2016.06.019 |
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| Abstract | •Control of chaotic systems is formulated as constrained optimization problem.•Teaching-learning-based optimization is to direct the chaotic series.•The efficacy of TLBO based optimal control technique have been demonstrated.
Control of chaotic systems to given targets is a subject of substantial and well-developed research issue in nonlinear science, which can be formulated as a class of multi-modal constrained numerical optimization problem with multi-dimensional decision variables. This investigation elucidates the feasibility of applying a novel population-based metaheuristics labeled here as Teaching-learning-based optimization to direct the orbits of discrete chaotic dynamical systems towards the desired target region. Several consecutive control steps of small bounded perturbations are made in the Teaching-learning-based optimization strategy to direct the chaotic series towards the optimal neighborhood of the desired target rapidly, where a conventional controller is effective for chaos control. Working with the dynamics of the well-known Hénon as well as Ushio discrete chaotic systems, we assess the effectiveness and efficiency of the Teaching-learning-based optimization based optimal control technique, meanwhile the impacts of the core parameters on performances are also discussed. Furthermore, possible engineering applications of directing chaotic orbits are discussed. |
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| AbstractList | Control of chaotic systems to given targets is a subject of substantial and well-developed research issue in nonlinear science, which can be formulated as a class of multi-modal constrained numerical optimization problem with multi-dimensional decision variables. This investigation elucidates the feasibility of applying a novel population-based metaheuristics labeled here as Teaching-learning-based optimization to direct the orbits of discrete chaotic dynamical systems towards the desired target region. Several consecutive control steps of small bounded perturbations are made in the Teaching-learning-based optimization strategy to direct the chaotic series towards the optimal neighborhood of the desired target rapidly, where a conventional controller is effective for chaos control. Working with the dynamics of the well-known Henon as well as Ushio discrete chaotic systems, we assess the effectiveness and efficiency of the Teaching-learning-based optimization based optimal control technique, meanwhile the impacts of the core parameters on performances are also discussed. Furthermore, possible engineering applications of directing chaotic orbits are discussed. •Control of chaotic systems is formulated as constrained optimization problem.•Teaching-learning-based optimization is to direct the chaotic series.•The efficacy of TLBO based optimal control technique have been demonstrated. Control of chaotic systems to given targets is a subject of substantial and well-developed research issue in nonlinear science, which can be formulated as a class of multi-modal constrained numerical optimization problem with multi-dimensional decision variables. This investigation elucidates the feasibility of applying a novel population-based metaheuristics labeled here as Teaching-learning-based optimization to direct the orbits of discrete chaotic dynamical systems towards the desired target region. Several consecutive control steps of small bounded perturbations are made in the Teaching-learning-based optimization strategy to direct the chaotic series towards the optimal neighborhood of the desired target rapidly, where a conventional controller is effective for chaos control. Working with the dynamics of the well-known Hénon as well as Ushio discrete chaotic systems, we assess the effectiveness and efficiency of the Teaching-learning-based optimization based optimal control technique, meanwhile the impacts of the core parameters on performances are also discussed. Furthermore, possible engineering applications of directing chaotic orbits are discussed. |
| Author | Liu, Bo Feng, Xiaoyi Li, Zhengyang Wang, Yudong Lyu, Xin |
| Author_xml | – sequence: 1 givenname: Yudong surname: Wang fullname: Wang, Yudong organization: Science and Technology on Space Physics Laboratory, Beijing, China – sequence: 2 givenname: Xiaoyi surname: Feng fullname: Feng, Xiaoyi organization: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China – sequence: 3 givenname: Xin surname: Lyu fullname: Lyu, Xin organization: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China – sequence: 4 givenname: Zhengyang surname: Li fullname: Li, Zhengyang organization: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China – sequence: 5 givenname: Bo surname: Liu fullname: Liu, Bo email: liub01@mails.tsinghua.edu.cn, bliu@amss.ac.cn organization: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China |
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| SubjectTerms | Chaos control Chaos theory Chaotic dynamics Computational intelligence Dynamical systems Dynamics Mathematical models Nonlinearity Optimization Orbits Strategy Teaching-learning-based optimization |
| Title | Optimal targeting of nonlinear chaotic systems using a novel evolutionary computing strategy |
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